Citizen assistance in support of the social participation of seniors with craniocerebral trauma.
Bibliographic record
Abstract
Highly valued by seniors, community integration has beneficial effects on their biopsychosocial health. However, not all of them have the same opportunities to engage in social participation activities, particularly those with traumatic brain injury (TBI) who are more likely to be limited in their abilities to participate in the life of the community. The most common initiatives to help these people consist of human assistance to overcome their limitations and which do not focus on skills promotion. In order to optimize the available services and resources, personalized citizen assistance for community integration (APIC) has been developed, implemented, and evaluated. This article documents the impact of APIC on the community integration of seniors with TBI. A thematic content analysis was conducted on data from semi-structured interviews with participants and from attendant logbooks. The results show three major spin-offs from APIC: better insurance and a greater sense of autonomy, a renewed taste for investing in meaningful leisure activities, and greater openness to new relationship opportunities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".